In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory...In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.展开更多
We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that prov...We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.展开更多
The incorporation of commercial flame retardants into fiber-reinforced polymer(FRP)composites has been proposed as a potential solution to improve the latter’s poor flame resistance.However,this approach often poses ...The incorporation of commercial flame retardants into fiber-reinforced polymer(FRP)composites has been proposed as a potential solution to improve the latter’s poor flame resistance.However,this approach often poses a challenge,as it can adversely affect the mechanical properties of the FRP.Thus,balancing the need for improved flame resistance with the preservation of mechanical integrity remains a complex issue in FRP research.Addressing this critical concern,this study introduces a novel additive system featuring a combination of one-dimensional(1D)hollow tubular structured halloysite nanotubes(HNTs)and two-dimensional(2D)polygonal flake-shaped nano kaolinite(NKN).By employing a 1D/2D hybrid kaolinite nanoclay system,this research aims to simultaneously improve the flame retardancy and mechanical properties.This innovative approach offers several advantages.During combustion and pyrolysis processes,the 1D/2D hybrid kaolinite nanoclay system proves effective in reducing heat release and volatile leaching.Furthermore,the system facilitates the formation of reinforcing skeletons through a crosslinking mechanism during pyrolysis,resulting in the development of a compact char layer.This char layer acts as a protective barrier,enhancing the material’s resistance to heat and flames.In terms of mechanical properties,the multilayered polygonal flake-shaped 2D NKN plays a crucial role by impeding the formation of cracks that typically arise from vulnerable areas,such as adhesive phase particles.Simultaneously,the 1D HNT bridges these cracks within the matrix,ensuring the structural integrity of the composite material.In an optimal scenario,the homogeneously distributed 1D/2D hybrid kaolinite nanoclays exhibit remarkable results,with a 51.0%improvement in mode II fracture toughness(GIIC),indicating increased resistance to crack propagation.In addition,there is a 34.5%reduction in total heat release,signifying improved flame retardancy.This study represents a significant step forward in the field of composite materials.The innovative use of hybrid low-dimensional nanomaterials offers a promising avenue for the development of multifunctional composites.By carefully designing and incorporating these nanoclays,researchers can potentially create a new generation of FRP composites that excel in both flame resistance and mechanical strength.展开更多
The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calcula...The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.展开更多
This editorial takes a deeper look at the insights provided by Soresi and Giannitrapani,which examined the therapeutic potential of glucagon-like peptide-1 receptor agonists(GLP-1RAs)for metabolic dysfunction-associat...This editorial takes a deeper look at the insights provided by Soresi and Giannitrapani,which examined the therapeutic potential of glucagon-like peptide-1 receptor agonists(GLP-1RAs)for metabolic dysfunction-associated fatty liver disease.We provide supplementary insights to their research,highlighting the broader systemic implications of GLP-1RAs,synthesizing the current understanding of their mechanisms and the trajectory of research in this field.GLP-1RAs are revolutionizing the treatment of type 2 diabetes mellitus and beyond.Beyond glycemic control,GLP-1RAs demonstrate cardiovascular and renal protective effects,offering potential in managing diabetic kidney disease alongside renin–angiotensin–aldosterone system inhibitors.Their role in bone metabolism hints at benefits for diabetic osteoporosis,while the neuroprotective properties of GLP-1RAs show promise in Alzheimer's disease treatment by modulating neuronal insulin signaling.Additionally,they improve hormonal and metabolic profiles in polycystic ovary syndrome.This editorial highlights the multifaceted mechanisms of GLP-1RAs,emphasizing the need for ongoing research to fully realize their therapeutic potential across a range of multisystemic diseases.展开更多
目的通过Meta分析探讨甲状腺癌组织中黏蛋白1(MUC1)表达的临床意义及其与各临床病理参数的相关性。方法检索维普、万方、中国知网、中国生物医学文献、PubMed、Web of Science、Embase、Cochrane中英文数据库中关于MUC1在甲状腺癌中表...目的通过Meta分析探讨甲状腺癌组织中黏蛋白1(MUC1)表达的临床意义及其与各临床病理参数的相关性。方法检索维普、万方、中国知网、中国生物医学文献、PubMed、Web of Science、Embase、Cochrane中英文数据库中关于MUC1在甲状腺癌中表达及其意义的文献,以比值比(OR)和95%置信区间(95%CI)为效应指标,采用STATA 14软件进行Meta分析,并对发表偏倚及敏感性进行检验。结果共纳入13项病例对照研究,甲状腺癌病例组共1157例,非甲状腺癌对照组共509例。结果显示,甲状腺癌病例组MUC1表达明显高于非甲状腺癌对照组(OR=9.78,95%CI:7.49~12.78,P<0.001);伴有淋巴结转移的甲状腺癌组织中MUC1表达高于无淋巴结转移的甲状腺癌组织(OR=3.08,95%CI:1.37~6.92,P<0.05);TNM分期为Ⅲ~Ⅳ期的甲状腺癌组织中MUC1表达高于TNM分期为Ⅰ~Ⅱ期的甲状腺癌组织(OR=1.88,95%CI:1.22~2.90,P<0.05)。结论与非甲状腺癌对照组比较,甲状腺癌病例组MUC1呈高表达,且MUC1高表达与淋巴结转移、TNM分期密切相关。MUC1与甲状腺癌的发生和发展存在相关性,有望成为甲状腺癌分子研究领域的一项新靶标。展开更多
The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-trigge...The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional approach.Marrying the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is established.Meanwhile,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data.Finally,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.展开更多
This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objecti...This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.展开更多
This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the co...This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the control objective. The DF mapping refers to the fact that the system state and input of each robot can be derived algebraically from the flat outputs of the leaders and the cooperative errors and their finite order derivatives. Based on the proposed swarm DF mapping, a distributed controller is designed. The distributed implementation of swarm DF mapping is achieved through observer design. The effectiveness of the proposed method is validated through a numerical simulation of quadrotor swarm synchronization.展开更多
A novel operation control method for relay protection in flexible DC distribution networks with distributed power supply is proposed to address the issue of inaccurate fault location during relay protection,leading to...A novel operation control method for relay protection in flexible DC distribution networks with distributed power supply is proposed to address the issue of inaccurate fault location during relay protection,leading to poor performance.The method combines a fault-tolerant fault location method based on long-term and short-term memory networks to accurately locate the fault section.Then,an operation control method for relay protection based on adaptive weight and whale optimization algorithm(WOA)is used to construct an objective function considering the shortest relay protection action time and the smallest impulse current.The adaptive weight and WOA are employed to obtain the optimal strategy for relay protection operation control,reducing the action time and impulse current.Experimental results demonstrate the effectiveness of the proposed method in accurately locating faults and improving relay protection performance.The longest operation time is reduced by 4.7023 s,and the maximum impulse current is limited to 0.3 A,effectively controlling the impact of large impulse currents and enhancing control efficiency.展开更多
Type 1 diabetes(T1D)is a chronic autoimmune condition that destroys insulinproducing beta cells in the pancreas,leading to insulin deficiency and hyperglycemia.The management of T1D primarily focuses on exogenous insu...Type 1 diabetes(T1D)is a chronic autoimmune condition that destroys insulinproducing beta cells in the pancreas,leading to insulin deficiency and hyperglycemia.The management of T1D primarily focuses on exogenous insulin replacement to control blood glucose levels.However,this approach does not address the underlying autoimmune process or prevent the progressive loss of beta cells.Recent research has explored the potential of glucagon-like peptide-1 receptor agonists(GLP-1RAs)as a novel intervention to modify the disease course and delay the onset of T1D.GLP-1RAs are medications initially developed for treating type 2 diabetes.They exert their effects by enhancing glucose-dependent insulin secretion,suppressing glucagon secretion,and slowing gastric emptying.Emerging evidence suggests that GLP-1RAs may also benefit the treatment of newly diagnosed patients with T1D.This article aims to highlight the potential of GLP-1RAs as an intervention to delay the onset of T1D,possibly through their potential immunomodulatory and anti-inflammatory effects and preservation of beta-cells.This article aims to explore the potential of shifting the paradigm of T1D management from reactive insulin replacement to proactive disease modification,which should open new avenues for preventing and treating T1D,improving the quality of life and long-term outcomes for individuals at risk of T1D.展开更多
文摘In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.
基金Project supported by the National Natural Science Foundation of China (Grant No.62073045)。
文摘We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.
基金supported by the National Natural Science Foundation of China(11872279,12172258,and 11625210).
文摘The incorporation of commercial flame retardants into fiber-reinforced polymer(FRP)composites has been proposed as a potential solution to improve the latter’s poor flame resistance.However,this approach often poses a challenge,as it can adversely affect the mechanical properties of the FRP.Thus,balancing the need for improved flame resistance with the preservation of mechanical integrity remains a complex issue in FRP research.Addressing this critical concern,this study introduces a novel additive system featuring a combination of one-dimensional(1D)hollow tubular structured halloysite nanotubes(HNTs)and two-dimensional(2D)polygonal flake-shaped nano kaolinite(NKN).By employing a 1D/2D hybrid kaolinite nanoclay system,this research aims to simultaneously improve the flame retardancy and mechanical properties.This innovative approach offers several advantages.During combustion and pyrolysis processes,the 1D/2D hybrid kaolinite nanoclay system proves effective in reducing heat release and volatile leaching.Furthermore,the system facilitates the formation of reinforcing skeletons through a crosslinking mechanism during pyrolysis,resulting in the development of a compact char layer.This char layer acts as a protective barrier,enhancing the material’s resistance to heat and flames.In terms of mechanical properties,the multilayered polygonal flake-shaped 2D NKN plays a crucial role by impeding the formation of cracks that typically arise from vulnerable areas,such as adhesive phase particles.Simultaneously,the 1D HNT bridges these cracks within the matrix,ensuring the structural integrity of the composite material.In an optimal scenario,the homogeneously distributed 1D/2D hybrid kaolinite nanoclays exhibit remarkable results,with a 51.0%improvement in mode II fracture toughness(GIIC),indicating increased resistance to crack propagation.In addition,there is a 34.5%reduction in total heat release,signifying improved flame retardancy.This study represents a significant step forward in the field of composite materials.The innovative use of hybrid low-dimensional nanomaterials offers a promising avenue for the development of multifunctional composites.By carefully designing and incorporating these nanoclays,researchers can potentially create a new generation of FRP composites that excel in both flame resistance and mechanical strength.
基金supported by the National Natural Science Foundation of China(52372310)the State Key Laboratory of Advanced Rail Autonomous Operation(RAO2023ZZ001)+1 种基金the Fundamental Research Funds for the Central Universities(2022JBQY001)Beijing Laboratory of Urban Rail Transit.
文摘The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.
基金Supported by National Natural Science Foundation of China,No.U23A20398 and No.82030007Sichuan Science and Technology Program,No.2022YFS0578.
文摘This editorial takes a deeper look at the insights provided by Soresi and Giannitrapani,which examined the therapeutic potential of glucagon-like peptide-1 receptor agonists(GLP-1RAs)for metabolic dysfunction-associated fatty liver disease.We provide supplementary insights to their research,highlighting the broader systemic implications of GLP-1RAs,synthesizing the current understanding of their mechanisms and the trajectory of research in this field.GLP-1RAs are revolutionizing the treatment of type 2 diabetes mellitus and beyond.Beyond glycemic control,GLP-1RAs demonstrate cardiovascular and renal protective effects,offering potential in managing diabetic kidney disease alongside renin–angiotensin–aldosterone system inhibitors.Their role in bone metabolism hints at benefits for diabetic osteoporosis,while the neuroprotective properties of GLP-1RAs show promise in Alzheimer's disease treatment by modulating neuronal insulin signaling.Additionally,they improve hormonal and metabolic profiles in polycystic ovary syndrome.This editorial highlights the multifaceted mechanisms of GLP-1RAs,emphasizing the need for ongoing research to fully realize their therapeutic potential across a range of multisystemic diseases.
文摘目的通过Meta分析探讨甲状腺癌组织中黏蛋白1(MUC1)表达的临床意义及其与各临床病理参数的相关性。方法检索维普、万方、中国知网、中国生物医学文献、PubMed、Web of Science、Embase、Cochrane中英文数据库中关于MUC1在甲状腺癌中表达及其意义的文献,以比值比(OR)和95%置信区间(95%CI)为效应指标,采用STATA 14软件进行Meta分析,并对发表偏倚及敏感性进行检验。结果共纳入13项病例对照研究,甲状腺癌病例组共1157例,非甲状腺癌对照组共509例。结果显示,甲状腺癌病例组MUC1表达明显高于非甲状腺癌对照组(OR=9.78,95%CI:7.49~12.78,P<0.001);伴有淋巴结转移的甲状腺癌组织中MUC1表达高于无淋巴结转移的甲状腺癌组织(OR=3.08,95%CI:1.37~6.92,P<0.05);TNM分期为Ⅲ~Ⅳ期的甲状腺癌组织中MUC1表达高于TNM分期为Ⅰ~Ⅱ期的甲状腺癌组织(OR=1.88,95%CI:1.22~2.90,P<0.05)。结论与非甲状腺癌对照组比较,甲状腺癌病例组MUC1呈高表达,且MUC1高表达与淋巴结转移、TNM分期密切相关。MUC1与甲状腺癌的发生和发展存在相关性,有望成为甲状腺癌分子研究领域的一项新靶标。
基金supported in part by the National Key Research and Development Program of China(2021YFB1714800)the National Natural Science Foundation of China(62088101,61925303,62173034,U20B2073)+1 种基金the Natural Science Foundation of Chongqing(2021ZX4100027)the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germanys Excellence Strategy—EXC 2075-390740016(468094890)。
文摘The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional approach.Marrying the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is established.Meanwhile,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data.Finally,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.
基金supported in part by the National Natural Science Foundation of China(NSFC)(61773260)the Ministry of Science and Technology (2018YFB130590)。
文摘This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.
基金Project supported by the National Natural Science Foundation of China (Nos. 62373025, 12332004,62003013, and 11932003)。
文摘This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the control objective. The DF mapping refers to the fact that the system state and input of each robot can be derived algebraically from the flat outputs of the leaders and the cooperative errors and their finite order derivatives. Based on the proposed swarm DF mapping, a distributed controller is designed. The distributed implementation of swarm DF mapping is achieved through observer design. The effectiveness of the proposed method is validated through a numerical simulation of quadrotor swarm synchronization.
文摘A novel operation control method for relay protection in flexible DC distribution networks with distributed power supply is proposed to address the issue of inaccurate fault location during relay protection,leading to poor performance.The method combines a fault-tolerant fault location method based on long-term and short-term memory networks to accurately locate the fault section.Then,an operation control method for relay protection based on adaptive weight and whale optimization algorithm(WOA)is used to construct an objective function considering the shortest relay protection action time and the smallest impulse current.The adaptive weight and WOA are employed to obtain the optimal strategy for relay protection operation control,reducing the action time and impulse current.Experimental results demonstrate the effectiveness of the proposed method in accurately locating faults and improving relay protection performance.The longest operation time is reduced by 4.7023 s,and the maximum impulse current is limited to 0.3 A,effectively controlling the impact of large impulse currents and enhancing control efficiency.
文摘Type 1 diabetes(T1D)is a chronic autoimmune condition that destroys insulinproducing beta cells in the pancreas,leading to insulin deficiency and hyperglycemia.The management of T1D primarily focuses on exogenous insulin replacement to control blood glucose levels.However,this approach does not address the underlying autoimmune process or prevent the progressive loss of beta cells.Recent research has explored the potential of glucagon-like peptide-1 receptor agonists(GLP-1RAs)as a novel intervention to modify the disease course and delay the onset of T1D.GLP-1RAs are medications initially developed for treating type 2 diabetes.They exert their effects by enhancing glucose-dependent insulin secretion,suppressing glucagon secretion,and slowing gastric emptying.Emerging evidence suggests that GLP-1RAs may also benefit the treatment of newly diagnosed patients with T1D.This article aims to highlight the potential of GLP-1RAs as an intervention to delay the onset of T1D,possibly through their potential immunomodulatory and anti-inflammatory effects and preservation of beta-cells.This article aims to explore the potential of shifting the paradigm of T1D management from reactive insulin replacement to proactive disease modification,which should open new avenues for preventing and treating T1D,improving the quality of life and long-term outcomes for individuals at risk of T1D.
